# RoboMIND 2.0: A Multimodal, Bimanual Mobile Manipulation Dataset for Generalizable Embodied Intelligence

arXiv: https://arxiv.org/abs/2512.24653 · HTML: https://mlsanity.com/paper/2512.24653/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 31 Dec 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 30 of 32)

- [Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization](https://mlsanity.com/paper/2601.12993/) (2026)
- [Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models](https://mlsanity.com/paper/2606.17846/) (2026)
- [World Model for Robot Learning: A Comprehensive Survey](https://mlsanity.com/paper/2605.00080/) (2026)
- [LaST0: Latent Spatio-Temporal Chain-of-Thought for Robotic Vision-Language-Action Model](https://mlsanity.com/paper/2601.05248/) (2026)
- [Wall-OSS-0.5 Technical Report](https://mlsanity.com/paper/2605.30877/) (2026)
- [ARM: Advantage Reward Modeling for Long-Horizon Manipulation](https://mlsanity.com/paper/2604.03037/) (2026)
- [HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation](https://mlsanity.com/paper/2604.07993/) (2026)
- [Rethinking Visual-Language-Action Model Scaling: Alignment, Mixture, and Regularization](https://mlsanity.com/paper/2602.09722/) (2026)
- [X-Tokenizer: A Multimodal Action Tokenizer for Vision-Language-Action Pretraining](https://mlsanity.com/paper/2606.14752/) (2026)
- [Data Pyramid for Embodied Manipulation: A Survey](https://mlsanity.com/paper/2607.24744/) (2026)
- [DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks](https://mlsanity.com/paper/2604.16484/) (2026)
- [DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter](https://mlsanity.com/paper/2602.05513/) (2026)
- [HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone](https://mlsanity.com/paper/2607.25895/) (2026)
- [HumanScale: Egocentric Human Video Can Outperform Real-Robot Data for Embodied Pretraining](https://mlsanity.com/paper/2606.20521/) (2026)
- [ACE-Data-0: Human-Centric Ambient Capture as Embodied Data Engine](https://mlsanity.com/paper/2607.28625/) (2026)
- [JoyAI-RA 0.5: Scaling Robot Manipulation Learning via Dual Action Alignment](https://mlsanity.com/paper/2608.05674/) (2026)
- [LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models](https://mlsanity.com/paper/2608.22403/) (2026)
- [Lift3D-VLA: Lifting VLA Models to 3D Geometry and Dynamics-Aware Manipulation](https://mlsanity.com/paper/2607.06564/) (2026)
- [Scaling Bimanual Household Manipulation from 1,500 hours of Demonstrations to On-Policy Corrections](https://mlsanity.com/paper/2609.03591/) (2026)
- [RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation](https://mlsanity.com/paper/2602.16444/) (2026)
- [Translation as a Bridging Action: Transferring Manipulation Skills from Humans to Robots](https://mlsanity.com/paper/2606.28133/) (2026)
- [MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence](https://mlsanity.com/paper/2609.25627/) (2026)
- [InternW0-Δ: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data](https://mlsanity.com/paper/2609.31394/) (2026)
- [In-Context Learning for Robots: Methods and Applications](https://mlsanity.com/paper/2609.36012/) (2026)
- [AFUN: Towards an Affordance Foundation Model for Functionality Understanding](https://mlsanity.com/paper/2606.02551/) (2026)
- [RoboInter1.5: A Holistic Intermediate Representation Suite for Embodied World Modeling and Robotic Manipulation](https://mlsanity.com/paper/2607.18709/) (2026)
- [Capek 0.5: An Execution-Centric Vision-Language Model for Embodied Intelligence](https://mlsanity.com/paper/2608.06756/) (2026)
- [WholeBodyWAM: Learning Whole-Body World Action Models with Scalable Motion Priors](https://mlsanity.com/paper/2609.18197/) (2026)
- [EVO-WAM: Evolving World Action Models through Video-Action Verification](https://mlsanity.com/paper/2609.38057/) (2026)
- [WorldLine: Action-Driven Visual Simulation for Robotic Manipulation](https://mlsanity.com/paper/2609.38059/) (2026)

## Abstract (arXiv)

While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstrations. Consequently, the ability of existing models to generalize across long-horizon bimanual tasks and mobile manipulation in unstructured environments remains limited. To bridge this gap, we present RoboMIND 2.0, a comprehensive real-world dataset comprising over 310K dual-arm manipulation trajectories collected across six distinct robot embodiments and 739 complex tasks. Crucially, to support research in contact-rich and spatially extended tasks, the dataset incorporates 12K tactile-enhanced episodes and 20K mobile manipulation trajectories. Complementing this physical data, we construct high-fidelity digital twins of our real-world environments, releasing an additional 20K-trajectory simulated dataset to facilitate robust sim-to-real transfer. To fully exploit the potential of RoboMIND 2.0, we propose MIND-2 system, a hierarchical dual-system frame-work optimized via offline reinforcement learning. MIND-2 integrates a high-level semantic planner (MIND-2-VLM) to decompose abstract natural language instructions into grounded subgoals, coupled with a low-level Vision-Language-Action executor (MIND-2-VLA), which generates precise, proprioception-aware motor actions.